Psychological experiments have provided convincing evidence that the functional development of the brain requires rich experience from infancy to adulthood. Neuroscientific studies have revealed rich biological detail about the brain, from mitosis, to cell migration and differentiation, to cortical wiring and patterning, to cortical responses and adaptation, and to the emergence of cell and cortical functions. Knowledge in computer science and artificial intelligence are necessary for understanding aspects of such systems in processing large scale, high dimensional sensory and motor data, including developmental paradigms, agent architectures, computational complexities, and the necessity of near optimality. Robotic investigations are amenable to understanding the necessity and problems of real sensors and effectors in dealing with the real physical world, through high-dimensional raw signals like pixel values and motor voltage.
Despite a promising beginning, pattern recognition software (e.g. object recognition, text understanding, and so on) using a “neural network” approach (inspired by the human brain) in general has encountered serious roadblocks limiting the rate of progress. Traditional methods cannot “attend” and “recognize” using the same network structure. For example, a system can find interesting regions, but cannot recognize objects. A system can only recognize objects that have already been segmented and separated from their natural background.